2026-04-17T17:14:24Zhttps://recercat.cat/oai/request

oai:recercat.cat:2445/1367202025-12-04T19:37:26Zcom_2072_1057col_2072_478813col_2072_478917

00925njm 22002777a 4500 dc Barbosa, Sergio author Pardo-Mates, Naiara author Hidalgo-Serrano, Míriam author Saurina, Javier author Puignou i Garcia, Lluís author Núñez Burcio, Oscar author 2019-07-08T11:55:49Z 2019-06-02 2019-07-08T11:55:49Z UHPLC-HRMS (Orbitrap) fingerprinting in negative and positive H-ESI mode was applied to the characterization, classification and authentication of cranberry-based natural and pharmaceutical products. HRMS data in full scan mode (m/z 100-1500) at a resolution of 70,000 full-width at half maximum was recorded and processed with MSConvert software to obtain a profile of peak intensities in function of m/z values and retention times. A threshold peak filter of absolute intensity (105 counts) was applied to reduce data complexity. Principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA) revealed patterns able to discriminate the analyzed samples according to the fruit of origin (cranberry, grape, blueberry and raspberry). Discrimination among cranberry-based natural and cranberry-based pharmaceutical preparations was also achieved. Both, UHPLC-HRMS fingerprints in negative and positive H-ESI modes, as well as the data fusion of both acquisition modes, showed to be good chemical descriptors to address cranberry extracts authentication. Validation of the proposed methodology showed a prediction rate of 100% of the samples. Obtained data was further treated by partial least squares (PLS) regression to identify frauds and quantify the percentage of adulterant fruits in cranberry-fruit extracts, achieving prediction errors in the range 0.17-3.86%. Espectrometria de masses Química dels aliments Mass spectrometry Food composition UHPLC-HRMS (Orbitrap) fingerprinting in the classification and authentication of cranberry-based natural products and pharmaceuticals using multivariate calibration methods